lebsral

Detect fraud, unusual behavior, and anomalies in events or transactions using AI. Use when detecting fraud, flagging suspicious transactions, anomaly detection in logs, spotting unusual user behavior, abuse detection, identifying outliers in data, suspicious activity monitoring, fraud scoring, unusual pattern detection, flagging account takeover attempts, detecting bot traffic, abnormal usage patt

Deep dive into dspy.ChatAdapter -- the default adapter that formats DSPy signatures into multi-turn chat messages with field delimiters, parses LM responses back into typed Python objects, and falls back to JSONAdapter on failure. Use when you need to understand how DSPy builds prompts, debug why a model ignores output format, customize prompt rendering, enable native function calling, use callbac

Stop your AI from making things up. Use when your AI hallucinates, fabricates facts, is not grounded in real data, does not cite sources, makes unsupported claims, or you need to verify AI responses against source material. Also use when your LLM makes up facts, responses are disconnected from the input, or outputs are not grounded in source documents. Covers citation enforcement, faithfulness ver

Use when you need to stream LM output tokens to a frontend in real time — progressive responses, typing indicators, server-sent events, or WebSocket feeds. Common scenarios - streaming DSPy responses to a React UI, FastAPI SSE endpoint with DSPy, showing AI typing in a chat interface, streaming multi-field outputs, streaming ReAct agent loop progress, or handling duplicate fields with predict_name

Use LangWatch for DSPy auto-tracing and real-time optimizer progress. Use when you want to set up LangWatch, langwatch.dspy.init, auto-tracing DSPy, real-time optimization dashboard, optimizer progress tracking, app.langwatch.ai, or DSPy optimizer dashboard. Also used for langwatch setup, pip install langwatch, langwatch trace, optimizer progress, real-time optimization, watch optimizer run, LangW

Build AI that answers questions about your database. Use when you need text-to-SQL, natural language database queries, a data assistant for non-technical users, AI-powered analytics, plain English database search, or a chatbot that talks to your database. Covers DSPy pipelines for schema understanding, SQL generation, validation, and result interpretation., text-to-SQL that actually works, AI SQL

Use when the agent task is best solved by writing and executing Python code — data manipulation, computation, file processing, or tasks where code is more reliable than natural language reasoning. Common scenarios - data analysis tasks where the agent writes pandas code, computation-heavy tasks where natural language reasoning fails, file processing automation, tasks requiring precise calculations

Use when you need structured source attribution in AI responses — verifiable citations that link claims to specific passages in source documents. Common scenarios - RAG with citation extraction, grounded answers with document references, legal or compliance use cases requiring source proof, building AI that cites its sources, or verifying which document an answer came from. Related - ai-stopping-h

Use when you want the highest-quality prompt optimization DSPy offers — jointly optimizes instructions and few-shot demos, with auto=light/medium/heavy presets. Common scenarios - you want the best possible accuracy from prompt optimization, jointly tuning instructions and few-shot demonstrations, using auto presets for different compute budgets, or when COPRO or BootstrapFewShot alone are not rea

Use when you need to connect DSPy agents to external MCP tool servers — databases, file systems, APIs, or any MCP-compatible service. Common scenarios - wiring MCP tools into a ReAct or CodeAct agent, discovering tools from an MCP server at runtime, converting MCP tools to DSPy tools, or building agents that use tools hosted on remote servers. Related - dspy-tools, dspy-react, dspy-codeact, ai-tak

Build AI that recommends products, articles, or content based on user preferences. Use when building product recommendations, you-might-also-like features, personalizing feeds, content discovery, ranking by user preference, suggesting related items, building a recommendation engine, collaborative filtering with LLMs, re-ranking search results by relevance, curating personalized playlists, next-bes

Use when you need to compose multiple DSPy calls into a pipeline — structuring multi-step programs as reusable, optimizable components with forward() logic. Common scenarios - building a multi-step pipeline as a class, composing Predict and ChainOfThought calls in sequence, creating reusable AI components, structuring a RAG pipeline as a module, or building nested programs where one module calls a